{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Statistics for Data Scientists (Python)\n",
    "# Chapter 5. Classification\n",
    "> (c) 2019 Peter C. Bruce, Andrew Bruce, Peter Gedeck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:14.194696Z",
     "iopub.status.busy": "2022-04-26T19:56:14.194382Z",
     "iopub.status.idle": "2022-04-26T19:56:15.799722Z",
     "shell.execute_reply": "2022-04-26T19:56:15.798716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no display found. Using non-interactive Agg backend\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression #, LogisticRegressionCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
    "from sklearn.metrics import roc_curve, accuracy_score, roc_auc_score\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, BorderlineSMOTE\n",
    "from pygam import LinearGAM, s, f, l\n",
    "\n",
    "\n",
    "from dmba import classificationSummary\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:15.803360Z",
     "iopub.status.busy": "2022-04-26T19:56:15.803149Z",
     "iopub.status.idle": "2022-04-26T19:56:16.169820Z",
     "shell.execute_reply": "2022-04-26T19:56:16.169119Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import common\n",
    "    DATA = common.dataDirectory()\n",
    "except ImportError:\n",
    "    DATA = Path().resolve() / 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define paths to data sets. If you don't keep your data in the same directory as the code, adapt the path names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.173097Z",
     "iopub.status.busy": "2022-04-26T19:56:16.172908Z",
     "iopub.status.idle": "2022-04-26T19:56:16.177543Z",
     "shell.execute_reply": "2022-04-26T19:56:16.176079Z"
    }
   },
   "outputs": [],
   "source": [
    "LOAN3000_CSV = DATA / 'loan3000.csv'\n",
    "LOAN_DATA_CSV = DATA / 'loan_data.csv.gz'\n",
    "FULL_TRAIN_SET_CSV = DATA / 'full_train_set.csv.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes\n",
    "## The Naive Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.181332Z",
     "iopub.status.busy": "2022-04-26T19:56:16.181157Z",
     "iopub.status.idle": "2022-04-26T19:56:16.431952Z",
     "shell.execute_reply": "2022-04-26T19:56:16.430978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted class:  default\n",
      "predicted probabilities\n",
      "    default  paid off\n",
      "0  0.653699  0.346301\n"
     ]
    }
   ],
   "source": [
    "loan_data = pd.read_csv(LOAN_DATA_CSV)\n",
    "\n",
    "# convert to categorical\n",
    "loan_data.outcome = loan_data.outcome.astype('category')\n",
    "loan_data.outcome.cat.reorder_categories(['paid off', 'default'])\n",
    "loan_data.purpose_ = loan_data.purpose_.astype('category')\n",
    "loan_data.home_ = loan_data.home_.astype('category')\n",
    "loan_data.emp_len_ = loan_data.emp_len_.astype('category')\n",
    "\n",
    "predictors = ['purpose_', 'home_', 'emp_len_']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(loan_data[predictors], prefix='', prefix_sep='', dtype=int)\n",
    "y = loan_data[outcome]\n",
    "\n",
    "naive_model = MultinomialNB(alpha=0.01, fit_prior=True)\n",
    "naive_model = MultinomialNB(alpha=1e-10, fit_prior=False)\n",
    "naive_model.fit(X, y)\n",
    "\n",
    "new_loan = X.loc[146:146, :]\n",
    "print('predicted class: ', naive_model.predict(new_loan)[0])\n",
    "\n",
    "probabilities = pd.DataFrame(naive_model.predict_proba(new_loan),\n",
    "                             columns=naive_model.classes_)\n",
    "print('predicted probabilities',)\n",
    "print(probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example not in book"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical variables are not supported in scikit-learn. The example would need to demonstrate binning a variable and display the probability distribution of the bins.\n",
    "```\n",
    "## example not in book\n",
    "less_naive <- NaiveBayes(outcome ~ borrower_score + payment_inc_ratio + \n",
    "                           purpose_ + home_ + emp_len_, data = loan_data)\n",
    "less_naive$table[1:2]\n",
    "\n",
    "png(filename=file.path(PSDS_PATH, 'figures', 'psds_naive_bayes.png'),  width = 4, height=3, units='in', res=300)\n",
    "\n",
    "stats <- less_naive$table[[1]]\n",
    "ggplot(data.frame(borrower_score=c(0,1)), aes(borrower_score)) +\n",
    "  stat_function(fun = dnorm, color='blue', linetype=1, \n",
    "                arg=list(mean=stats[1, 1], sd=stats[1, 2])) +\n",
    "  stat_function(fun = dnorm, color='red', linetype=2, \n",
    "                arg=list(mean=stats[2, 1], sd=stats[2, 2])) +\n",
    "  labs(y='probability')\n",
    "dev.off()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminant Analysis\n",
    "## A Simple Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.435118Z",
     "iopub.status.busy": "2022-04-26T19:56:16.434917Z",
     "iopub.status.idle": "2022-04-26T19:56:16.454130Z",
     "shell.execute_reply": "2022-04-26T19:56:16.453281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          0\n",
      "borrower_score     7.175839\n",
      "payment_inc_ratio -0.099676\n"
     ]
    }
   ],
   "source": [
    "loan3000 = pd.read_csv(LOAN3000_CSV)\n",
    "loan3000.outcome = loan3000.outcome.astype('category')\n",
    "\n",
    "predictors = ['borrower_score', 'payment_inc_ratio']\n",
    "outcome = 'outcome'\n",
    "\n",
    "X = loan3000[predictors]\n",
    "y = loan3000[outcome]\n",
    "\n",
    "loan_lda = LinearDiscriminantAnalysis()\n",
    "loan_lda.fit(X, y)\n",
    "print(pd.DataFrame(loan_lda.scalings_, index=X.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.457102Z",
     "iopub.status.busy": "2022-04-26T19:56:16.456899Z",
     "iopub.status.idle": "2022-04-26T19:56:16.465568Z",
     "shell.execute_reply": "2022-04-26T19:56:16.464796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    default  paid off\n",
      "0  0.553544  0.446456\n",
      "1  0.558953  0.441047\n",
      "2  0.272696  0.727304\n",
      "3  0.506254  0.493746\n",
      "4  0.609952  0.390048\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(loan_lda.predict_proba(loan3000[predictors]),\n",
    "                    columns=loan_lda.classes_)\n",
    "print(pred.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.468298Z",
     "iopub.status.busy": "2022-04-26T19:56:16.468090Z",
     "iopub.status.idle": "2022-04-26T19:56:16.744368Z",
     "shell.execute_reply": "2022-04-26T19:56:16.743532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use scalings and center of means to determine decision boundary\n",
    "center = np.mean(loan_lda.means_, axis=0)\n",
    "slope = - loan_lda.scalings_[0] / loan_lda.scalings_[1]\n",
    "intercept = center[1] - center[0] * slope\n",
    "\n",
    "# payment_inc_ratio for borrower_score of 0 and 20\n",
    "x_0 = (0 - intercept) / slope\n",
    "x_20 = (20 - intercept) / slope\n",
    "\n",
    "lda_df = pd.concat([loan3000, pred['default']], axis=1)\n",
    "lda_df.head()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "g = sns.scatterplot(x='borrower_score', y='payment_inc_ratio',\n",
    "                    hue='default', data=lda_df, \n",
    "                    palette=sns.diverging_palette(240, 10, n=9, as_cmap=True),\n",
    "                    ax=ax, legend=False)\n",
    "\n",
    "ax.set_ylim(0, 20)\n",
    "ax.set_xlim(0.15, 0.8)\n",
    "ax.plot((x_0, x_20), (0, 20), linewidth=3)\n",
    "ax.plot(*loan_lda.means_.transpose())\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression\n",
    "## Logistic Response Function and Logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:16.747624Z",
     "iopub.status.busy": "2022-04-26T19:56:16.747355Z",
     "iopub.status.idle": "2022-04-26T19:56:17.031969Z",
     "shell.execute_reply": "2022-04-26T19:56:17.031129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.arange(0.01, 1, 0.01)\n",
    "df = pd.DataFrame({\n",
    "    'p': p,\n",
    "    'logit': np.log(p / (1 - p)),\n",
    "    'odds': p / (1 - p),\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax.axhline(0, color='grey', linestyle='--')\n",
    "ax.axvline(0.5, color='grey', linestyle='--')\n",
    "ax.plot(df['p'], df['logit'])\n",
    "ax.set_xlabel('Probability')\n",
    "ax.set_ylabel('logit(p)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression and the GLM\n",
    "The package _scikit-learn_ has a specialised class for `LogisticRegression`. _Statsmodels_ has a more general method based on generalized linear model (GLM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.037884Z",
     "iopub.status.busy": "2022-04-26T19:56:17.036980Z",
     "iopub.status.idle": "2022-04-26T19:56:17.160300Z",
     "shell.execute_reply": "2022-04-26T19:56:17.159611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  -1.6380882883923482\n",
      "classes ['default' 'paid off']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <td>-0.079728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>borrower_score</th>\n",
       "      <td>4.611037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_consolidation</th>\n",
       "      <td>-0.249342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_improvement</th>\n",
       "      <td>-0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_purchase</th>\n",
       "      <td>-0.229376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical</th>\n",
       "      <td>-0.510087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>-0.620534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_business</th>\n",
       "      <td>-1.215662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>-0.048453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>-0.157355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt; 1 Year</th>\n",
       "      <td>0.357463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       coeff\n",
       "payment_inc_ratio  -0.079728\n",
       "borrower_score      4.611037\n",
       "debt_consolidation -0.249342\n",
       "home_improvement   -0.407614\n",
       "major_purchase     -0.229376\n",
       "medical            -0.510087\n",
       "other              -0.620534\n",
       "small_business     -1.215662\n",
       "OWN                -0.048453\n",
       "RENT               -0.157355\n",
       " > 1 Year           0.357463"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = ['payment_inc_ratio', 'purpose_', 'home_', 'emp_len_', \n",
    "              'borrower_score']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(loan_data[predictors], prefix='', prefix_sep='', \n",
    "                   drop_first=True, dtype=int)\n",
    "y = loan_data[outcome] # .cat.categories\n",
    "\n",
    "logit_reg = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "logit_reg.fit(X, y)\n",
    "\n",
    "print('intercept ', logit_reg.intercept_[0])\n",
    "print('classes', logit_reg.classes_)\n",
    "pd.DataFrame({'coeff': logit_reg.coef_[0]}, \n",
    "             index=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the intercept and coefficients are reversed compared to the R model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.163690Z",
     "iopub.status.busy": "2022-04-26T19:56:17.163015Z",
     "iopub.status.idle": "2022-04-26T19:56:17.169389Z",
     "shell.execute_reply": "2022-04-26T19:56:17.168700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['credit_card', 'debt_consolidation', 'home_improvement',\n",
      "       'major_purchase', 'medical', 'other', 'small_business'],\n",
      "      dtype='object')\n",
      "Index(['MORTGAGE', 'OWN', 'RENT'], dtype='object')\n",
      "Index([' < 1 Year', ' > 1 Year'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(loan_data['purpose_'].cat.categories)\n",
    "print(loan_data['home_'].cat.categories)\n",
    "print(loan_data['emp_len_'].cat.categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Not in book_ :\n",
    "If you have a feature or outcome variable that is ordinal, use the scikit-learn `OrdinalEncoder` to replace the categories (here, 'paid off' and 'default') with numbers. In the below code, we replace 'paid off' with 0 and 'default' with 1. This reverses the order of the predicted classes and as a consequence, the coefficients will be reversed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.172930Z",
     "iopub.status.busy": "2022-04-26T19:56:17.172507Z",
     "iopub.status.idle": "2022-04-26T19:56:17.270480Z",
     "shell.execute_reply": "2022-04-26T19:56:17.269865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  1.6378909416318836\n",
      "classes [0. 1.]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <td>0.079739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>borrower_score</th>\n",
       "      <td>-4.612183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_consolidation</th>\n",
       "      <td>0.249414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_improvement</th>\n",
       "      <td>0.407734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_purchase</th>\n",
       "      <td>0.229710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical</th>\n",
       "      <td>0.510744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0.620800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_business</th>\n",
       "      <td>1.214936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>0.048211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>0.157288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt; 1 Year</th>\n",
       "      <td>-0.356794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       coeff\n",
       "payment_inc_ratio   0.079739\n",
       "borrower_score     -4.612183\n",
       "debt_consolidation  0.249414\n",
       "home_improvement    0.407734\n",
       "major_purchase      0.229710\n",
       "medical             0.510744\n",
       "other               0.620800\n",
       "small_business      1.214936\n",
       "OWN                 0.048211\n",
       "RENT                0.157288\n",
       " > 1 Year          -0.356794"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "enc = OrdinalEncoder(categories=[['paid off', 'default']])\n",
    "y_enc = enc.fit_transform(loan_data[[outcome]]).ravel()\n",
    "\n",
    "logit_reg_enc = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "logit_reg_enc.fit(X, y_enc)\n",
    "\n",
    "print('intercept ', logit_reg_enc.intercept_[0])\n",
    "print('classes', logit_reg_enc.classes_)\n",
    "pd.DataFrame({'coeff': logit_reg_enc.coef_[0]}, \n",
    "             index=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted Values from Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.273633Z",
     "iopub.status.busy": "2022-04-26T19:56:17.273409Z",
     "iopub.status.idle": "2022-04-26T19:56:17.293082Z",
     "shell.execute_reply": "2022-04-26T19:56:17.291698Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            default      paid off\n",
      "count  45342.000000  45342.000000\n",
      "mean      -0.757850     -0.760423\n",
      "std        0.378032      0.390419\n",
      "min       -2.768873     -3.538865\n",
      "25%       -0.985728     -0.977164\n",
      "50%       -0.697366     -0.688946\n",
      "75%       -0.472209     -0.467076\n",
      "max       -0.029476     -0.064787\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(logit_reg.predict_log_proba(X),\n",
    "                    columns=logit_reg.classes_)\n",
    "print(pred.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.297687Z",
     "iopub.status.busy": "2022-04-26T19:56:17.296627Z",
     "iopub.status.idle": "2022-04-26T19:56:17.318641Z",
     "shell.execute_reply": "2022-04-26T19:56:17.317742Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            default      paid off\n",
      "count  45342.000000  45342.000000\n",
      "mean       0.500001      0.499999\n",
      "std        0.167336      0.167336\n",
      "min        0.062733      0.029046\n",
      "25%        0.373167      0.376377\n",
      "50%        0.497895      0.502105\n",
      "75%        0.623623      0.626833\n",
      "max        0.970954      0.937267\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(logit_reg.predict_proba(X),\n",
    "                    columns=logit_reg.classes_)\n",
    "print(pred.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the Coefficients and Odds Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.322842Z",
     "iopub.status.busy": "2022-04-26T19:56:17.322222Z",
     "iopub.status.idle": "2022-04-26T19:56:17.453280Z",
     "shell.execute_reply": "2022-04-26T19:56:17.452416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax.plot(df['logit'], df['odds'])\n",
    "ax.set_xlabel('log(odds ratio)')\n",
    "ax.set_ylabel('odds ratio')\n",
    "ax.set_xlim(0, 5.1)\n",
    "ax.set_ylim(-5, 105)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the Model\n",
    "For comparison, here the GLM model using _statsmodels_. This method requires that the outcome is mapped to numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.456596Z",
     "iopub.status.busy": "2022-04-26T19:56:17.456391Z",
     "iopub.status.idle": "2022-04-26T19:56:17.566111Z",
     "shell.execute_reply": "2022-04-26T19:56:17.565125Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                45342\n",
      "Model:                            GLM   Df Residuals:                    45330\n",
      "Model Family:                Binomial   Df Model:                           11\n",
      "Link Function:                  Logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -28757.\n",
      "Date:                Wed, 24 May 2023   Deviance:                       57515.\n",
      "Time:                        12:46:35   Pearson chi2:                 4.54e+04\n",
      "No. Iterations:                     4   Pseudo R-squ. (CS):             0.1112\n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "payment_inc_ratio      0.0797      0.002     32.058      0.000       0.075       0.085\n",
      "borrower_score        -4.6126      0.084    -55.203      0.000      -4.776      -4.449\n",
      "debt_consolidation     0.2494      0.028      9.030      0.000       0.195       0.303\n",
      "home_improvement       0.4077      0.047      8.747      0.000       0.316       0.499\n",
      "major_purchase         0.2296      0.054      4.277      0.000       0.124       0.335\n",
      "medical                0.5105      0.087      5.882      0.000       0.340       0.681\n",
      "other                  0.6207      0.039     15.738      0.000       0.543       0.698\n",
      "small_business         1.2153      0.063     19.192      0.000       1.091       1.339\n",
      "OWN                    0.0483      0.038      1.271      0.204      -0.026       0.123\n",
      "RENT                   0.1573      0.021      7.420      0.000       0.116       0.199\n",
      " > 1 Year             -0.3567      0.053     -6.779      0.000      -0.460      -0.254\n",
      "const                  1.6381      0.074     22.224      0.000       1.494       1.783\n",
      "======================================================================================\n"
     ]
    }
   ],
   "source": [
    "# use GLM (general linear model) with the binomial family to \n",
    "# fit a logistic regression\n",
    "y_numbers = [1 if yi == 'default' else 0 for yi in y]\n",
    "logit_reg_sm = sm.GLM(y_numbers, X.assign(const=1), \n",
    "                      family=sm.families.Binomial())\n",
    "logit_result = logit_reg_sm.fit()\n",
    "print(logit_result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use splines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.569719Z",
     "iopub.status.busy": "2022-04-26T19:56:17.569089Z",
     "iopub.status.idle": "2022-04-26T19:56:17.891474Z",
     "shell.execute_reply": "2022-04-26T19:56:17.890590Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Generalized Linear Model Regression Results                             \n",
      "=====================================================================================================\n",
      "Dep. Variable:     ['outcome[default]', 'outcome[paid off]']   No. Observations:                45342\n",
      "Model:                                                   GLM   Df Residuals:                    45321\n",
      "Model Family:                                       Binomial   Df Model:                           20\n",
      "Link Function:                                         Logit   Scale:                          1.0000\n",
      "Method:                                                 IRLS   Log-Likelihood:                -28731.\n",
      "Date:                                       Wed, 24 May 2023   Deviance:                       57462.\n",
      "Time:                                               12:46:36   Pearson chi2:                 4.54e+04\n",
      "No. Iterations:                                            6   Pseudo R-squ. (CS):             0.1122\n",
      "Covariance Type:                                   nonrobust                                         \n",
      "==================================================================================================\n",
      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------\n",
      "Intercept                          1.5756      0.331      4.765      0.000       0.928       2.224\n",
      "purpose_[T.debt_consolidation]     0.2486      0.028      8.998      0.000       0.194       0.303\n",
      "purpose_[T.home_improvement]       0.4097      0.047      8.757      0.000       0.318       0.501\n",
      "purpose_[T.major_purchase]         0.2382      0.054      4.416      0.000       0.132       0.344\n",
      "purpose_[T.medical]                0.5206      0.087      5.980      0.000       0.350       0.691\n",
      "purpose_[T.other]                  0.6284      0.040     15.781      0.000       0.550       0.706\n",
      "purpose_[T.small_business]         1.2250      0.063     19.305      0.000       1.101       1.349\n",
      "home_[T.OWN]                       0.0498      0.038      1.309      0.191      -0.025       0.124\n",
      "home_[T.RENT]                      0.1577      0.021      7.431      0.000       0.116       0.199\n",
      "emp_len_[T. > 1 Year]             -0.3526      0.053     -6.699      0.000      -0.456      -0.249\n",
      "bs(payment_inc_ratio, df=8)[0]     0.7042      0.342      2.060      0.039       0.034       1.374\n",
      "bs(payment_inc_ratio, df=8)[1]     0.6621      0.198      3.351      0.001       0.275       1.049\n",
      "bs(payment_inc_ratio, df=8)[2]     0.8118      0.245      3.309      0.001       0.331       1.293\n",
      "bs(payment_inc_ratio, df=8)[3]     1.0377      0.223      4.644      0.000       0.600       1.476\n",
      "bs(payment_inc_ratio, df=8)[4]     1.1901      0.233      5.112      0.000       0.734       1.646\n",
      "bs(payment_inc_ratio, df=8)[5]     2.8404      0.316      8.980      0.000       2.220       3.460\n",
      "bs(payment_inc_ratio, df=8)[6]    -1.3427      1.229     -1.092      0.275      -3.752       1.067\n",
      "bs(payment_inc_ratio, df=8)[7]     7.1094      6.393      1.112      0.266      -5.420      19.639\n",
      "bs(borrower_score, df=3)[0]       -2.9011      0.533     -5.448      0.000      -3.945      -1.857\n",
      "bs(borrower_score, df=3)[1]       -2.6056      0.196    -13.284      0.000      -2.990      -2.221\n",
      "bs(borrower_score, df=3)[2]       -5.7421      0.508    -11.313      0.000      -6.737      -4.747\n",
      "==================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "formula = ('outcome ~ bs(payment_inc_ratio, df=8) + purpose_ + ' +\n",
    "           'home_ + emp_len_ + bs(borrower_score, df=3)')\n",
    "model = smf.glm(formula=formula, data=loan_data, family=sm.families.Binomial())\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:17.895385Z",
     "iopub.status.busy": "2022-04-26T19:56:17.894779Z",
     "iopub.status.idle": "2022-04-26T19:56:18.270716Z",
     "shell.execute_reply": "2022-04-26T19:56:18.269717Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.genmod.generalized_linear_model import GLMResults\n",
    "def partialResidualPlot(model, df, outcome, feature, fig, ax):\n",
    "    y_actual = [0 if s == 'default' else 1 for s in df[outcome]]\n",
    "    y_pred = model.predict(df)\n",
    "    org_params = model.params.copy()\n",
    "    zero_params = model.params.copy()\n",
    "    # set model parametes of other features to 0\n",
    "    for i, name in enumerate(zero_params.index):\n",
    "        if feature in name:\n",
    "            continue\n",
    "        zero_params[i] = 0.0\n",
    "    model.initialize(model.model, zero_params)\n",
    "    feature_prediction = model.predict(df)\n",
    "    ypartial = -np.log(1/feature_prediction - 1)\n",
    "    ypartial = ypartial - np.mean(ypartial)\n",
    "    model.initialize(model.model, org_params)\n",
    "    results = pd.DataFrame({\n",
    "        'feature': df[feature],\n",
    "        'residual': -2 * (y_actual - y_pred),\n",
    "        'ypartial': ypartial/ 2,\n",
    "    })\n",
    "    results = results.sort_values(by=['feature'])\n",
    "\n",
    "    ax.scatter(results.feature, results.residual, marker=\".\", s=72./fig.dpi)\n",
    "    ax.plot(results.feature, results.ypartial, color='black')\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel(f'Residual + {feature} contribution')\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "partialResidualPlot(results, loan_data, 'outcome', 'payment_inc_ratio', fig, ax)\n",
    "ax.set_xlim(0, 25)\n",
    "ax.set_ylim(-2.5, 2.5)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Classification Models\n",
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:18.274333Z",
     "iopub.status.busy": "2022-04-26T19:56:18.274152Z",
     "iopub.status.idle": "2022-04-26T19:56:18.291371Z",
     "shell.execute_reply": "2022-04-26T19:56:18.290358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Yhat = default  Yhat = paid off\n",
      "Y = default            14336             8335\n",
      "Y = paid off            8148            14523\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "pred = logit_reg.predict(X)\n",
    "pred_y = logit_reg.predict(X) == 'default'\n",
    "true_y = y == 'default'\n",
    "true_pos = true_y & pred_y\n",
    "true_neg = ~true_y & ~pred_y\n",
    "false_pos = ~true_y & pred_y\n",
    "false_neg = true_y & ~pred_y\n",
    "\n",
    "conf_mat = pd.DataFrame([[np.sum(true_pos), np.sum(false_neg)], [np.sum(false_pos), np.sum(true_neg)]],\n",
    "                       index=['Y = default', 'Y = paid off'],\n",
    "                       columns=['Yhat = default', 'Yhat = paid off'])\n",
    "print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:18.296845Z",
     "iopub.status.busy": "2022-04-26T19:56:18.294975Z",
     "iopub.status.idle": "2022-04-26T19:56:18.463313Z",
     "shell.execute_reply": "2022-04-26T19:56:18.462640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14336  8335]\n",
      " [ 8148 14523]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y, logit_reg.predict(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The package _dmba_ contains the function `classificationSummary` that prints confusion matrix and accuracy for a classification model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:18.467518Z",
     "iopub.status.busy": "2022-04-26T19:56:18.467302Z",
     "iopub.status.idle": "2022-04-26T19:56:18.752266Z",
     "shell.execute_reply": "2022-04-26T19:56:18.751356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Accuracy 0.6365)\n",
      "\n",
      "         Prediction\n",
      "  Actual  default paid off\n",
      " default    14336     8335\n",
      "paid off     8148    14523\n"
     ]
    }
   ],
   "source": [
    "classificationSummary(y, logit_reg.predict(X), \n",
    "                      class_names=logit_reg.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision, Recall, and Specificity\n",
    "The _scikit-learn_ function `precision_recall_fscore_support` returns\n",
    "precision, recall, fbeta_score and support. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:18.755964Z",
     "iopub.status.busy": "2022-04-26T19:56:18.755747Z",
     "iopub.status.idle": "2022-04-26T19:56:18.912153Z",
     "shell.execute_reply": "2022-04-26T19:56:18.911570Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision 0.6376089663760897\n",
      "Recall 0.6323496978518812\n",
      "Specificity 0.6405981209474659\n"
     ]
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(y, logit_reg.predict(X))\n",
    "print('Precision', conf_mat[0, 0] / sum(conf_mat[:, 0]))\n",
    "print('Recall', conf_mat[0, 0] / sum(conf_mat[0, :]))\n",
    "print('Specificity', conf_mat[1, 1] / sum(conf_mat[1, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:18.914750Z",
     "iopub.status.busy": "2022-04-26T19:56:18.914511Z",
     "iopub.status.idle": "2022-04-26T19:56:19.213578Z",
     "shell.execute_reply": "2022-04-26T19:56:19.212679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.63760897, 0.63535742]),\n",
       " array([0.6323497 , 0.64059812]),\n",
       " array([0.63496844, 0.63796701]),\n",
       " array([22671, 22671]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_recall_fscore_support(y, logit_reg.predict(X), \n",
    "                                labels=['default', 'paid off'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve\n",
    "The function `roc_curve` in _Scikit-learn_ calculates all the information that is required for plotting a ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.216320Z",
     "iopub.status.busy": "2022-04-26T19:56:19.216088Z",
     "iopub.status.idle": "2022-04-26T19:56:19.374369Z",
     "shell.execute_reply": "2022-04-26T19:56:19.373157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y, logit_reg.predict_proba(X)[:, 0], \n",
    "                                 pos_label='default')\n",
    "roc_df = pd.DataFrame({'recall': tpr, 'specificity': 1 - fpr})\n",
    "\n",
    "ax = roc_df.plot(x='specificity', y='recall', figsize=(4, 4), legend=False)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_xlim(1, 0)\n",
    "ax.plot((1, 0), (0, 1))\n",
    "ax.set_xlabel('specificity')\n",
    "ax.set_ylabel('recall')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUC\n",
    "Accuracy can easily be calculated using the _scikit-learn_ function `accuracy_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.377721Z",
     "iopub.status.busy": "2022-04-26T19:56:19.377392Z",
     "iopub.status.idle": "2022-04-26T19:56:19.414489Z",
     "shell.execute_reply": "2022-04-26T19:56:19.413330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.691710795288669\n",
      "0.6917108731135808\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(roc_df.recall[:-1] * np.diff(1 - roc_df.specificity)))\n",
    "print(roc_auc_score([1 if yi == 'default' else 0 for yi in y], logit_reg.predict_proba(X)[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.419623Z",
     "iopub.status.busy": "2022-04-26T19:56:19.418737Z",
     "iopub.status.idle": "2022-04-26T19:56:19.569010Z",
     "shell.execute_reply": "2022-04-26T19:56:19.568191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y, logit_reg.predict_proba(X)[:,0], \n",
    "                                 pos_label='default')\n",
    "roc_df = pd.DataFrame({'recall': tpr, 'specificity': 1 - fpr})\n",
    "\n",
    "ax = roc_df.plot(x='specificity', y='recall', figsize=(4, 4), legend=False)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_xlim(1, 0)\n",
    "# ax.plot((1, 0), (0, 1))\n",
    "ax.set_xlabel('specificity')\n",
    "ax.set_ylabel('recall')\n",
    "ax.fill_between(roc_df.specificity, 0, roc_df.recall, alpha=0.3)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strategies for Imbalanced Data\n",
    "## Undersampling\n",
    "> The results differ from the R version, however are equivalent to results obtained using the R code. Model based results are of similar magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.572345Z",
     "iopub.status.busy": "2022-04-26T19:56:19.572125Z",
     "iopub.status.idle": "2022-04-26T19:56:19.840546Z",
     "shell.execute_reply": "2022-04-26T19:56:19.839547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(119987, 19)\n"
     ]
    }
   ],
   "source": [
    "full_train_set = pd.read_csv(FULL_TRAIN_SET_CSV)\n",
    "print(full_train_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.844147Z",
     "iopub.status.busy": "2022-04-26T19:56:19.843743Z",
     "iopub.status.idle": "2022-04-26T19:56:19.856631Z",
     "shell.execute_reply": "2022-04-26T19:56:19.855802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.894546909248504\n",
      "percentage of loans in default:  None\n"
     ]
    }
   ],
   "source": [
    "print('percentage of loans in default: ', \n",
    "print(      100 * np.mean(full_train_set.outcome == 'default')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:19.859257Z",
     "iopub.status.busy": "2022-04-26T19:56:19.859062Z",
     "iopub.status.idle": "2022-04-26T19:56:20.719833Z",
     "shell.execute_reply": "2022-04-26T19:56:20.718606Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9759390600648404\n",
      "percentage of loans predicted to default:  None\n"
     ]
    }
   ],
   "source": [
    "predictors = ['payment_inc_ratio', 'purpose_', 'home_', 'emp_len_', \n",
    "              'dti', 'revol_bal', 'revol_util']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(full_train_set[predictors], prefix='', prefix_sep='', \n",
    "                   drop_first=True, dtype=int)\n",
    "y = full_train_set[outcome]\n",
    "\n",
    "full_model = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "full_model.fit(X, y)\n",
    "print('percentage of loans predicted to default: ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:20.723284Z",
     "iopub.status.busy": "2022-04-26T19:56:20.722757Z",
     "iopub.status.idle": "2022-04-26T19:56:20.742561Z",
     "shell.execute_reply": "2022-04-26T19:56:20.741585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.360375747224595"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.mean(full_train_set.outcome == 'default') / \n",
    " np.mean(full_model.predict(X) == 'default'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oversampling and Up/Down Weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:20.746431Z",
     "iopub.status.busy": "2022-04-26T19:56:20.745350Z",
     "iopub.status.idle": "2022-04-26T19:56:21.586120Z",
     "shell.execute_reply": "2022-04-26T19:56:21.585263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.79836148916132\n",
      "percentage of loans predicted to default (weighting):  None\n"
     ]
    }
   ],
   "source": [
    "default_wt = 1 / np.mean(full_train_set.outcome == 'default')\n",
    "wt = [default_wt if outcome == 'default' else 1 for outcome in full_train_set.outcome]\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X, y, wt)\n",
    "print('percentage of loans predicted to default (weighting): ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generation\n",
    "The package _imbalanced-learn_ provides an implementation of the _SMOTE_ and similar algorithms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:21.589636Z",
     "iopub.status.busy": "2022-04-26T19:56:21.589086Z",
     "iopub.status.idle": "2022-04-26T19:56:26.221456Z",
     "shell.execute_reply": "2022-04-26T19:56:26.220452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of loans in default (SMOTE resampled):  50.0\n",
      "percentage of loans predicted to default (SMOTE):  29.31900955936893\n",
      "percentage of loans in default (ADASYN resampled):  48.56040383751355\n",
      "27.57382049722053\n",
      "percentage of loans predicted to default (ADASYN):  None\n"
     ]
    }
   ],
   "source": [
    "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
    "print('percentage of loans in default (SMOTE resampled): ', \n",
    "      100 * np.mean(y_resampled == 'default'))\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X_resampled, y_resampled)\n",
    "print('percentage of loans predicted to default (SMOTE): ', \n",
    "      100 * np.mean(full_model.predict(X) == 'default'))\n",
    "\n",
    "\n",
    "X_resampled, y_resampled = ADASYN().fit_resample(X, y)\n",
    "print('percentage of loans in default (ADASYN resampled): ', \n",
    "      100 * np.mean(y_resampled == 'default'))\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X_resampled, y_resampled)\n",
    "print('percentage of loans predicted to default (ADASYN): ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:26.226370Z",
     "iopub.status.busy": "2022-04-26T19:56:26.225034Z",
     "iopub.status.idle": "2022-04-26T19:56:26.638161Z",
     "shell.execute_reply": "2022-04-26T19:56:26.637239Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
      "   max_iter=100, scale=None, terms=s(0) + s(1) + intercept, \n",
      "   tol=0.0001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "loan3000 = pd.read_csv(LOAN3000_CSV)\n",
    "\n",
    "predictors = ['borrower_score', 'payment_inc_ratio']\n",
    "outcome = 'outcome'\n",
    "\n",
    "X = loan3000[predictors]\n",
    "y = loan3000[outcome]\n",
    "\n",
    "loan_tree = DecisionTreeClassifier(random_state=1, criterion='entropy', \n",
    "                                   min_impurity_decrease=0.003)\n",
    "loan_tree.fit(X, y)\n",
    "\n",
    "loan_lda = LinearDiscriminantAnalysis()\n",
    "loan_lda.fit(X, y)\n",
    "\n",
    "logit_reg = LogisticRegression(penalty=\"l2\", solver='liblinear')\n",
    "logit_reg.fit(X, y)\n",
    "\n",
    "\n",
    "## model\n",
    "gam = LinearGAM(s(0) + s(1))\n",
    "print(gam.gridsearch(X.values, [1 if yi == 'default' else 0 for yi in y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:26.642958Z",
     "iopub.status.busy": "2022-04-26T19:56:26.642003Z",
     "iopub.status.idle": "2022-04-26T19:56:27.029777Z",
     "shell.execute_reply": "2022-04-26T19:56:27.028334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    'Decision Tree': loan_tree,\n",
    "    'Linear Discriminant Analysis': loan_lda,\n",
    "    'Logistic Regression': logit_reg,\n",
    "    'Generalized Additive Model': gam,\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(5, 5))\n",
    "\n",
    "xvalues = np.arange(0.25, 0.73, 0.005)\n",
    "yvalues = np.arange(-0.1, 20.1, 0.1)\n",
    "xx, yy = np.meshgrid(xvalues, yvalues)\n",
    "X = pd.DataFrame({\n",
    "    'borrower_score': xx.ravel(),\n",
    "    'payment_inc_ratio': yy.ravel(),\n",
    "})\n",
    "\n",
    "boundary = {}\n",
    "\n",
    "for n, (title, model) in enumerate(models.items()):\n",
    "    ax = axes[n // 2, n % 2]\n",
    "    predict = model.predict(X)\n",
    "    if 'Generalized' in title:\n",
    "        Z = np.array([1 if z > 0.5 else 0 for z in predict])\n",
    "    else:\n",
    "        \n",
    "        Z = np.array([1 if z == 'default' else 0 for z in predict])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    boundary[title] = yvalues[np.argmax(Z > 0, axis=0)]\n",
    "    boundary[title][Z[-1,:] == 0] = yvalues[-1]\n",
    "\n",
    "    c = ax.pcolormesh(xx, yy, Z, cmap='Blues', vmin=0.1, vmax=1.3, shading='auto')\n",
    "    ax.set_title(title)\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:27.033503Z",
     "iopub.status.busy": "2022-04-26T19:56:27.033290Z",
     "iopub.status.idle": "2022-04-26T19:56:27.171134Z",
     "shell.execute_reply": "2022-04-26T19:56:27.170483Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boundary['borrower_score'] = xvalues\n",
    "boundaries = pd.DataFrame(boundary)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "boundaries.plot(x='borrower_score', ax=ax)\n",
    "ax.set_ylabel('payment_inc_ratio')\n",
    "ax.set_ylim(0, 20)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
